Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications by Ke He, Thang Xuan Vu, Lisheng Fan, Symeon Chatzinotas, Bjorn Ottersten

“…This limitation reduces their prediction accuracy in spatio-temporal predictive learning, where understanding both spatial and temporal dependencies is essential. …”
Get full text

Drone Attitude and Position Prediction via Stacked Hybrid Deep Learning Model for Massive MIMO Applications by Abdullah Al-Ahmadi

“…This paper presents a novel stacked hybrid deep learning model for real-time prediction of drone attitude and position, specifically designed to support applications in massive Multiple-Input Multiple-Output (MIMO) systems. …”
Get full text

Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model by Shengchao Zhao, Evan T. Keller, Tyler Robinson, Jinlu Dai, Alyssa Ghose, Ajjai Alva, Trachette Jackson, Harsh Vardhan Jain

Get full text

Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction by Shihu Jiao, Xiucai Ye, Tetsuya Sakurai, Quan Zou, Wu Han, Chao Zhan

“…By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. …”
Get full text

A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data by Ningqing Pang, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, Jeremy Cheuk-Hin Leung

“…The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25° × 0.25°). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. …”
Get full text

AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review by Tiong Kah Yong, Zhenliang Ma, Carl-William Palmqvist

“…The framework covers six key aspects across overall, spatial, temporal, and train-specific dimensions, providing a systematic approach for the comprehensive assessment of train delay prediction models. …”
Get full text

Calculation Methods of Topographic Factors Modification Using Data Digital Elevation Model (DEM) To Predict Erosion by Hengki Simanjuntak, Hendrayanto ., Nining Puspaningsih

Get full text

Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 by Chaibo Jose Armando, Joacim Rocklöv, Mohsin Sidat, Yesim Tozan, Alberto Francisco Mavume, Maquins Odhiambo Sewe

“…This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). …”
Get full text

A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

“…This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. …”
Get full text

Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models by Assaf Shmuel, Teddy Lazebnik, Oren Glickman, Eyal Heifetz, Colin Price

“…In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. …”
Get full text

A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction by Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang, Haifeng Song, Lunming Qin

“…The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. …”
Get full text

Exploring malaria prediction models in Togo: a time series forecasting by health district and target group by Muriel Rabilloud, Nicolas Voirin, Anne Thomas, Tchaa Abalo Bakai, Tinah Atcha-Oubou, Tchassama Tchadjobo

“…Objectives Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. …”
Get full text

Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

“…This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. …”
Get full text

Cultivated Land Suitability Prediction in Southern Xinjiang Typical Areas Based on Optimized MaxEnt Model by Yilong Tian, Xiaohuang Liu, Hongyu Li, Run Liu, Ping Zhu, Chaozhu Li, Xinping Luo, Chao Wang, Honghui Zhao

Get full text

PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…Supervised principal component analysis and random survival forests were incorporated into the final model, which showed strong predictive ability in classifying patients. …”
Get full text

Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma by Juan I. Pisula, Doris Helbig, Lucas Sancéré, Oana-Diana Persa, Corinna Bürger, Anne Fröhlich, Carina Lorenz, Sandra Bingmann, Dennis Niebel, Konstantin Drexler, Jennifer Landsberg, Roman Thomas, Katarzyna Bozek, Johannes Brägelmann

“…Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. …”
Get full text

High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling by Chuang Tu, Jiayi Liu, Jing Wang, Jing Bai, Guangwen Hu

Get full text

Uncertainties in predicting the medium-term future occurrence of grapevine budburst in Europe: the role of phenological models and climate scenarios by Sergi Costafreda-Aumedes, Luisa Leolini, Christoph Menz, Chenyao Yang, Giovanni Argenti, Camilla Dibari, Marco Bindi, Helder Fraga, Joao Santos, Daniel Molitor, J. Arturo Torres-Matallana, Marko Simeunović, Marco Moriondo

Get full text

A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis by I Gede Nyoman Mindra Jaya, Yudhie Andriyana, Bertho Tantular, Farah Kristiani

“… The main objective of spatiotemporal analysis is to offer precise predictions of outcomes. The objective of this study is to assess the accuracy of the Bayesian Latent Gaussian Model in predicting outcomes by utilizing both time-varying and fixed spatial weight matrices. …”
Get full text

Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma by Rong Su, Yong Du, Pan Tian, Weifang Ma, Yongfeng Hui, Shaoqi Yang

“…DiscussionOur study reveals that the RMRM risk model could effectively predict the prognosis of HCC, and SPP1+ macrophages regulated by miR-4739-RNA methylation promote the proliferation and migration of HCC cells. …”
Get full text